Multimodal Deep Learning for Lymph Node Metastasis Prediction and Physician Performance Assessment in T1 Gastric Cancer
Development and Validation of a Multimodal Artificial Intelligence Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer and Its Impact on Physician Diagnostic Performance
Qun Zhao
300 participants
Jan 1, 2025
OBSERVATIONAL
Conditions
Summary
This study aims to develop and validate an artificial intelligence (AI) model that integrates clinical, pathological, and imaging data to predict the presence of lymph node metastasis (LNM) in patients with T1-stage gastric cancer. The study will also compare the diagnostic performance of physicians with and without AI assistance, including clinicians with varying levels of experience. The goal is to improve early decision-making and support more personalized treatment strategies for patients with early gastric cancer.
Eligibility
Inclusion Criteria6
- Age 18 years or older
- Histologically confirmed primary gastric adenocarcinoma
- Clinical stage T1 (T1a or T1b) confirmed by endoscopy and imaging
- Undergoing radical gastrectomy with lymph node dissection
- Preoperative data available: clinical variables, CT imaging, and pathology slides
- Written informed consent provided
Exclusion Criteria6
- History of other malignancies within the past 5 years
- Received neoadjuvant chemotherapy or radiotherapy
- Incomplete clinical or pathological data
- Poor quality or missing CT or histopathology images
- Patients with distant metastasis (M1) at diagnosis
- Inability or refusal to provide informed consent
Interventions
This intervention involves the use of a custom-built artificial intelligence (AI) diagnostic model that integrates multimodal data-including clinical variables, histopathological features, and imaging data-to predict lymph node metastasis in patients with T1-stage gastric cancer. The model provides risk probability scores and classification outputs that assist physicians in diagnostic decision-making. The AI system will be compared with physician performance at different levels of experience (resident, attending, senior) to assess its impact on diagnostic accuracy and clinical decision support.
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT07124754